I would like to ignore the errors that may occur when a batch is commited. In my case, unique columns.
The OLE DB Destination Error Output is set to "Ignore failure" but it is still failling. The Data Flow "stop on failure properties" are set to false and the MaximumErrorCount to 0.
I don't want to do row redirection to be able to keep the fast load mode.
Thank you
A few comments:
You can't use ignore errors because ignore row errors still passes the records to the destination. You have to use redirection to get rid of the bad rows.
If you don't want to keep a copy of the bad rows, then you can send them to a Row Count transformation since that has minimal performance impact. Alternatively, you can output the bad rows to a flat file or to another table so you can review the errors on a future date.
Fast load options are properties of the destination and not the source. You can use fast load even if you redirect error rows from your source to somewhere else. I just ran a performance test on a million row data set with the fast load ORDER option and the performance was basically identical when I added error redirection and redirected 500K rows to a Row Count transformation. I also verified performance was slower when I removed the fast load option, so I'm certain this has 0 impact.
I finally redirected the error stream into a test node (to test the errorcode and check that it is a row insertion error) that redirects these rows into a "OLE DB Query" node in which I do nothing with SELECT 1, to simply ignore them.
Related
I'm attempting to migrate IngestionTime (_PARTITIONTIME) to TIMESTAMP partitioned tables in BQ. In doing so, I also need to add several required columns. However, when I flip the switch and redirect my dataflow to the new TIMESTAMP partitioned table, it breaks. Things to note:
Approximately two million rows (likely one batch) is successfully inserted. The job continues to run but doesn't insert anything after that.
The job runs in batches.
My project is entirely in Java
When I run it as streaming, it appears to work as intended. Unfortunately, it's not practical for my use case and batch is required.
I've been investigating the issue for a couple of days and tried to break down the transition into the smallest steps possible. It appears that the step responsible for the error is introducing REQUIRED variables (it works fine when the same variables are NULLABLE). To avoid any possible parsing errors, I've set default values for all of the REQUIRED variables.
At the moment, I get the following combination of errors and I'm not sure how to address any of them:
The first error, repeats infrequently but usually in groups:
Profiling Agent not found. Profiles will not be available from this
worker
Occurs a lot and in large groups:
Can't verify serialized elements of type BoundedSource have well defined equals method. This may produce incorrect results on some PipelineRunner
Appears to be one very large group of these:
Aborting Operations. java.lang.RuntimeException: Unable to read value from state
Towards the end, this error appears every 5 minutes only surrounded by mild parsing errors described below.
Processing stuck in step BigQueryIO.Write/BatchLoads/SinglePartitionWriteTables/ParMultiDo(WriteTables) for at least 20m00s without outputting or completing in state finish
Due to the sheer volume of data my project parses, there are several parsing errors such as Unexpected character. They're rare but shouldn't break data insertion. If they do, I have a bigger problem as the data I collect changes frequently and I can adjust the parser only after I see the error, and therefore, see the new data format. Additionally, this doesn't cause the ingestiontime table to break (or my other timestamp partition tables to break). That being said, here's an example of a parsing error:
Error: Unexpected character (',' (code 44)): was expecting double-quote to start field name
EDIT:
Some relevant sample code:
public PipelineResult streamData() {
try {
GenericSection generic = new GenericSection(options.getBQProject(), options.getBQDataset(), options.getBQTable());
Pipeline pipeline = Pipeline.create(options);
pipeline.apply("Read PubSub Events", PubsubIO.readMessagesWithAttributes().fromSubscription(options.getInputSubscription()))
.apply(options.getWindowDuration() + " Windowing", generic.getWindowDuration(options.getWindowDuration()))
.apply(generic.getPubsubToString())
.apply(ParDo.of(new CrowdStrikeFunctions.RowBuilder()))
.apply(new BigQueryBuilder().setBQDest(generic.getBQDest())
.setStreaming(options.getStreamingUpload())
.setTriggeringFrequency(options.getTriggeringFrequency())
.build());
return pipeline.run();
}
catch (Exception e) {
LOG.error(e.getMessage(), e);
return null;
}
Writing to BQ. I did try to set the partitoning field here directly, but it didn't seem to affect anything:
BigQueryIO.writeTableRows()
.to(BQDest)
.withMethod(Method.FILE_LOADS)
.withNumFileShards(1000)
.withTriggeringFrequency(this.triggeringFrequency)
.withTimePartitioning(new TimePartitioning().setType("DAY"))
.withWriteDisposition(BigQueryIO.Write.WriteDisposition.WRITE_APPEND)
.withCreateDisposition(BigQueryIO.Write.CreateDisposition.CREATE_NEVER);
}
After a lot of digging, I found the error. I had parsing logic (a try/catch) that returned nothing (essentially a null row) in the event there was a parsing error. This would break BigQuery as my schema had several REQUIRED rows.
Since my job ran in batches, even one null row would cause the entire batch job to fail and not insert anything. This also explains why streaming inserted just fine. I'm surprised that BigQuery didn't throw an error claiming that I was attempting to insert a null into a required field.
In reaching this conclusion, I also realized that setting the partition field in my code was also necessary as opposed to just in the schema. It could be done using
.setField(partitionField)
I have a pentaho transformation, which is used to read a text file, to check some conditions( from which you can have errors, such as the number should be a positive number). From this errors I'm creating an excel file and I need for my job the number of the lines in this error file plus to log which lines were with problem.
The problem is that sometimes I have an error " the return values id can't be found in the input row".
This error is not every time. The job is running every night and sometimes it can work without any problems like one month and in one sunny day I just have this error.
I don't think that this is from the file, because if I execute the job again with the same file it is working. I can't understand what is the reason to fail, because it is saying the value "id", but I don't have such a value/column. Why it is searching a value, which doesn't exists.
Another strange thing is that normally the step, which fails should be executed at all( as far as I know), because no errors were found, so we don't have rows at all to this step.
Maybe the problem is connected with the "Prioritize Stream" step? Here I'm getting all errors( which use exactly the same columns). I tried before the grouping steps to put a sorting, but it didn't help. Now I'm thinking to try with "Blocking step".
The problem is that I don't know why this happen and how to fix it. Any suggestions?
see here
Check if all your aggregates ins the Group by step have a name.
However, sometimes the error comes from a previous step: the group (count...) request data from the Prioritize Stream, and if that step has an error, the error gets reported mistakenly as coming from the group rather than from the Prioritze.
Also, you mention a step which should not be executed because there is no data: I do not see any Filter which would prevent rows with missing id to flow from the Prioritize to the count.
This is a bug. It happens randomly in one of my transformations that often ends up with empty stream (no rows). It mostly works, but once in a while it gives this error. It seems to only fail when the stream is empty though.
Is there a way to prevent screen output for the query --destination_table?
I wan to move data sets through the workflow, but not necessarily see the all the rows
bug on job_73d3dffab7974d9db360f5c31a3a9fa7
This is a known issue, we'll fix it in the next version of bq. To work around, you can add --max_rows=0. This only changes the number of rows that get sent back, not the number of rows that get returned by the query (you can use LIMIT N for that in the query).
I'm using Azure Table Storage.
Let's say i have a Partition in my Table with 10,000 records, and I would like to get records number 1000 to 1999. And next time i would like to get records number 4000 to 4999 etc.
What is the fastest way of doing that?
All I can find till now are two options, which I don't like very much:
1. run a query which returns all 10,000 records, and filter out what I want when I get all 10,000 records.
2. Run a query whichs returns 1000 records at a time, and use a continuation token to get the next 1000 records.
Is it possible to get a continuation token without downloading all corresponding records? It would be great if i can get Continuation Token 1, than get Continuation token 2, and with CT2 get records 2000 to 2999.
Theoretically you should be able to use continuation tokens without downloading the actual data for the first 1000 recors by closing the connection you have after the first request. And I mean closing it at TCP level. And before you read all data. Then open a new connection and use continuation token there. Two WebRequests will not do it since the HTTP implementation will likely use keep alive wchich means all your data is going to be read in the background even though you don't read it in your code. Actually you can configure your HTTP requests to not use keep alive.
However, another way is naturally if you know the RowKey and can search on that but I assume you don't know which row keys will be in each 1000 entity batch.
Last I would ask why you have this problem in the first place. And what your access pattern is. If inserts are common and getting these records is rare I wouldn't bother making it more efficient. if this is like a paging problem i would probably get all data on the first request and cache it (in the cloud). if inserts are rare but you need to run this query often I would consider making the insertion of data have one partion for every 1000 entities and rebalance as needed (due to sorting) as entities are inserted.
OrPresumably due to inconsistent configuration of logging devices, I need to load a collection of csv files via SSIS that will sometimes have a first row with column names and will sometimes not. The file format is otherwise identical.
There seems a chance that the logging configuration can be standardized, so I don't want to waste programming time with a script task that opens each file and determines whether it has a header row and then processes it differently depending.
Rather, I would like to specify something like Destination.MaxNumberOfErrors, that would allow up to one error row per file (so if the only problem in the file was the header, it would not fail). The Flat File Source error is fatal though, so I don't see a way of getting it to keep going.
The meaning of the failure code is defined by the component, but the
error is fatal and the pipeline stopped executing. There may be error
messages posted before this with more information about the failure.
My best choice seems to be to simply ignore the first data row for now and wait to see if a more uniform configuration can be achieved. Of course, the dataset is invalid while this strategy is in place. I should add that the data is very big, so the ETL routines need to be as efficient as possible. In my opinion this contraindicates any file parsing or conditional splitting if there is any alternative.
The question is if there is a way to configure the File Source to continue from this fatal error?
Yes there is!
In the "Error Output" page in the editor, change the Error response for each row to "Redirect row". Then you can trap the problem rows (the headers, in your case) by taking them as a single column through the error output of your source.
If you can assume the values for header names would never appear in your data, then define your flat file connection manager as having no headers. The first step inside your data flow would check the values of column 1-N vs the header row values. Only let the data flow through if the values don't match.
Is there something more complex to the problem than that?